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Qogita is building an operating system for the global wholesale market, a $50 trillion industry that still operates largely offline. The company connects buyers, sellers, and distributors through a single order book with real-time market data, intelligent matching, and self-learning systems. Currently, 95% of transactions run without human intervention, and the business is doubling annually.
As a Data Scientist, you will own end-to-end data science work across Qogita's wholesale marketplace. You'll frame ambiguous commercial problems, build production-ready models, and maintain them in live systems. The Data Science team partners with Product, Engineering, Finance, and Commercial to build the intelligence layer powering pricing, matching, demand forecasting, and product discovery.
Key responsibilities:
- Build and deliver data science solutions across the stack: predictive models, ranking systems, demand forecasting, segmentation, pricing optimization, experimentation frameworks, and LLM-powered features based on business priorities
- Own business-critical ML systems end-to-end: problem framing, model design, deployment, monitoring, and ongoing maintenance in production
- Translate ambiguous business problems into tractable ML or statistical problems with clear success criteria, working closely with Product and Commercial teams
- Apply quantitative methods (regression, causal inference, classical ML, deep learning) to pricing, demand, liquidity, supplier matching, and catalogue enrichment
- Design and analyze experiments and A/B tests, ensuring statistical validity and translating results into actionable recommendations
- Collaborate with Engineers to ship models via reproducible MLOps workflows: experiment tracking, model serving, alerting, and production monitoring
- Communicate model choices, limitations, and trade-offs clearly to both technical and non-technical stakeholders
Requirements:
- 3+ years as a data scientist, applied ML engineer, or quantitative analyst with meaningful exposure across ML methods and statistical modelling
- Track record of owning models in production (not just building them): maintaining, monitoring, and iterating as live infrastructure
- Solid grounding in ML and stats fundamentals: probability, supervised and unsupervised learning, measurement and validation discipline
- Strong Python and SQL; comfortable with large transactional datasets and common DS/ML libraries (pandas, scikit-learn, XGBoost, PyTorch or similar)
- Experience collaborating on MLOps-style workflows (experiment tracking, serving, monitoring) and shipping with engineers
- Ability to communicate uncertainty and model limitations clearly to technical and non-technical audiences
- Bachelor's or Master's in a quantitative field (Data Science, Statistics, Economics, Econometrics, Mathematics, CS, or related), or equivalent experience
Nice to have:
- Depth in pricing and market economics: price theory, buyer behaviour, demand estimation, causal inference, experimentation with real commercial stakes
- Production LLM systems: RAG, evaluation frameworks, prompt/fine-tuning trade-offs, transformers and major model families, LangChain or similar
- Marketplace or B2B dynamics experience
- AWS/GCP/Azure ML infrastructure
- Tools: Airflow, Docker, dbt, Snowflake, Lightdash or similar
